{"id":"W2040263203","doi":"10.1016/s0273-1177(01)00305-2","title":"RADARSAT-1 image quality and calibration — a continuing success","year":2001,"lang":"en","type":"article","venue":"Advances in Space Research","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Remote sensing; Radiometric calibration; Calibration; Computer science; Image quality; Satellite; Data quality; Radiometry; Environmental science; Image (mathematics); Computer vision; Geography; Statistics; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008066971,0.00009011752,0.0001437028,0.0001508352,0.00006045615,0.00005596201,0.000136341,0.00006204708,0.00002508788],"category_scores_gemma":[0.0001211045,0.00008608923,0.0000158617,0.0004702505,0.0001501945,0.000421823,0.00004684039,0.0002650105,0.000003592668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006683324,"about_ca_system_score_gemma":0.000008904373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001481411,"about_ca_topic_score_gemma":0.0001687194,"domain_scores_codex":[0.9989997,0.00008746983,0.0001727325,0.0001979014,0.0002490369,0.0002931914],"domain_scores_gemma":[0.9993234,0.0003121626,0.00001645733,0.0002347952,0.00005618739,0.0000570492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001687497,0.00002651029,0.01329736,0.00007630265,0.000004418959,0.00001188956,0.0003276151,0.00001066079,0.002532806,0.01485237,0.0004339954,0.9684092],"study_design_scores_gemma":[0.0002591822,0.00002464257,0.004194482,0.00008757402,0.000001827845,0.0000142023,0.0005027828,0.01120741,0.01629252,0.01338305,0.9538262,0.0002060582],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1515562,0.01465695,0.802081,0.001808979,0.00006500216,0.0005987015,0.00000900738,0.0003861054,0.02883795],"genre_scores_gemma":[0.7691328,0.011402,0.2192335,0.000009181038,0.00005151393,0.00003628113,0.000004902414,0.00002232001,0.0001075404],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9682031,"threshold_uncertainty_score":0.3510617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02911267247093827,"score_gpt":0.3878183834843958,"score_spread":0.3587057110134575,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}